A simulation-based approach to modeling component interactions during design of flapping wing aerial vehicles. (July 2019)

• Record Type: Journal Article
• Authors: Gerdes, John; Bruck, Hugh A; Gupta, Satyandra K
• Is Part Of: International journal of micro air vehicles. Volume 11(2019)
• Publication Date: 2019-07
• Subjects: Flapping -- wing -- raven -- interactions -- system -- subsystem -- aeroelastic -- battery; Aerosonde (Drone aircraft) -- Periodicals; Aerospace engineering -- Periodicals; Aerosonde (Drone aircraft); Aerospace engineering; Periodicals; 623.746905
• DOI: 10.1177/1756829318822325
• Languages: English
• ISSNs: 1756-8293

Abstract

A new flapping wing aerial vehicle (FWAV) simulation methodology is presented that combines models of the key subsystems: (1) the actuator, (2) the battery, and (3) the wings. This approach captures component interactions that are inherently coupled in order to realize system-level designs for optimal system performance. The approach demonstrates that coupling between wing sizing, flapping motions, and loading conditions propagate into the motor–battery model to alter system-level performance properties. For the actuator subsystem model, a generalized servo motor using empirically derived coefficients to describe torque and angular velocity bandwidth in terms of voltage and current. This model is coupled with a lithium polymer battery model accounting for the nonlinear voltage drop and capacity derating effects associated with loading conditions. For aerodynamic predictions of the wing subsystem, a blade element model for predicting aerodynamic forces is coupled with an elastic wing deformation model that accounts for bending and twisting of the blade elements. System-level performance is then modeled in a design case study by coupling all of the subsystem models to account for relevant interactions, which generates a design trade space spanning a range of wing sizes, airspeeds, and flapping condition. The results from the simulation offer insight into vehicle configuration settings that provide maximum performance in terms of lift and endurance for the Robo Raven II flapping wing aerial vehicle. Experimental validation of the modeling approach shows good predictive accuracy. In addition, the presented framework offers a generalized approach for coupling interacting subsystems to improve overall predictive accuracy and identify areas where component-level improvements may offer system-level performance gains.